A Deep Learning Framework for Accurate Ocean Acoustic Sound Speed Profile Retrieval from Synergistic Satellite Remote Sensing Data

This project introduces a high-fidelity and scalable framework for the accurate retrieval of Ocean Acoustic Sound Speed Profiles (SSPs), which are vital for enhancing underwater acoustic applications like communication and navigation. The work overcomes the challenges of high cost and limited coverage associated with traditional methods by employing Synergistic Fusion of diverse ocean observations with advanced Deep Neural Networks (DNNs) (represented by machine learning models like MLP and XGBoost).